Explorative Sampling of Natural Mineral Resource Deposits

ABSTRACT

Methods and systems for improving mineral resource exploration and resource classification efficiency are provided herein. The generation and iterative, dynamic improvement of drill plans for sampling a target volume using drill holes is described. Methods and systems for the development and optimization of drill plans are able to accommodate a wide variety of constraints and targets, providing drill plans which aim to minimize the amount of explorative drilling while substantially converting unclassified sub-volumes, and in particular high-desirability sub-volumes, of the target volume to a specified or desired level while attempting to maximize targeted resource conversion efficiency. Resulting drill plans may provide a proposed collection of drill holes, defined in 3D space, penetrating the target volume which sufficiently sample a target volume while remaining within one or more specified constraints.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.16/063,117, filed Jun. 15, 2018, which is a U.S. National Phase ofPCT/CA2016/51494, filed Dec. 16, 2016, which claims priority to CanadianApplication No. 2915802, filed Dec. 18, 2015, the entire disclosures ofwhich are hereby incorporated herein by reference.

FIELD OF INVENTION

The present invention relates generally to methods and systems relatingto explorative sampling of target volumes. More specifically, thepresent invention relates to methods and systems for the development ofdrill plans for sampling a target volume such as a potential or existingmining site or mineral resource deposit.

BACKGROUND

Mining and natural resource recovery typically involves theidentification and characterization of natural resource deposits.Developing an understanding of the size, shape, positioning of naturalmineral resource deposits within a target volume, mineral content, andas well as an understanding of any geological features of the targetvolume such as contacts, faults, and other geological structures orfeatures, can be highly useful for natural resource recovery operationssuch as mining. Natural resource deposits are, however, often locateddeep underground, difficult to access, and difficult to assess. As such,significant difficulty and expense is often associated with theidentification and characterization of potential natural resourcedeposits.

Traditionally, exploratory drill holes have been used to sample targetvolumes in an effort to better understand potential natural resourcedeposits. These drill holes are typically long drill holes whichpenetrate the target volume, recovering samples of the target volumewhich when analysed provide information about the region of the targetvolume through which the drill hole passes. This information is thenused to create an estimate of the mineral content of the completevolume, and to identify the location of geological structures, contacts,and faults. These may be used to bound the volume. Exploratory drillingcan be quite expensive and as such, it is desirable to minimize thenumber and/or total length of drill holes used, and avoid drilling holesfrom positions which are costly, time consuming, or otherwise difficultto access or drill.

Conventionally, exploratory drilling operations have been designed bymanual analysis and selection of drill hole placement and orientation ata target volume. Efforts are made to efficiently sample the targetvolume, however manual analysis and selection can be time consuming, andoften does not identify optimized or near optimized solutions for drillhole placement and orientation. Although advances have since been madein the development and optimization of exploratory drilling plans, thisstill remains a significant challenge in the field.

Examples of methods and techniques used for the exploratoryinvestigation of mineral deposits are described in, for example,Prospecting and Exploration of Mineral Deposits (Bohmer and Kucera,Elsevier, Developments in Economic Geology (21), 2^(nd) Ed., 1986),which is herein incorporated by reference in its entirety.

Alternative, additional, and/or improved methods and systems fordeveloping explorative sampling drill plans for investigating naturalresource deposits is desirable.

SUMMARY OF INVENTION

In an embodiment, the invention provides a method for improving mineralresource exploration and resource classification efficiency, said methodcomprising:

-   -   defining a one or more target volumes of interest in 3D space;    -   segmenting the one or more target volumes into sub-volumes to        which one or more attributes indicating relative desirability        may be assigned; and    -   using an algorithmic solver to iteratively or programmatically        generate and improve a drill plan which aims to provide an        optimal or near optimal solution for drill hole distribution        within the one or more target volumes such that all or nearly        all the sub-volumes, or at least the sub-volumes of greatest        desirability, of the one or more target volumes are sampled to a        specified or desired level;

wherein the resulting drill plan comprises a collection of one or moreplanned drill holes which are defined in 3D space, and

wherein a set of operational constraints constrains the iteratively orprogrammatically generated and improved drill plan.

In another embodiment of a method as described above, the drill holes ofthe drill plan may be characterized by collar (start 3D coordinates) andeither a trace for deviated holes or a dip/azimuth and length forstraight holes.

In another embodiment of a method as described above, the set ofoperational constraints may comprise a historical drill hole locationsconstraint, a potential drilling setup location constraint, a drillingdirection constraint, a drilling dips constraint, a drilling azimuthconstraint, a drilling budget constraint, a sampling requirementconstraint, a drilling setup availability constraint, a constraintregarding distribution of drill holes from setups, a constraintregarding the total amount of surface ground disturbance, atopographical constraint, an environmental constraint, a constraintregarding environmental exclusion zones, a geological fault constraint,a geological contacts constraint, a geological structure constraint, ora constraint regarding existing underground workings or operations, orany combination thereof.

In still another embodiment of any of the method or methods describedabove, the set of operation constraints may comprise a potentialdrilling setup location constraint, a drilling dip constraint, and adrilling azimuth constraint.

In yet another embodiment of any of the method or methods describedabove, the algorithmic solver may be based on a heuristic or linearalgorithm; a metaheuristic algorithm; a metaheuristic SCP algorithm; alocalized random search; a modified random search; a taboo search; or anannealing algorithm.

In another embodiment of any of the method or methods described above,the one or more attributes indicating relative desirability of asub-volume may be selected from one or more of:

-   -   distance of the sub-volume from an existing or planned drill        hole;    -   estimate variance;    -   grade estimates;    -   mining development or production schedules and timing;    -   bounding of the sub-volume by site specific geological contacts,        structures, or faults;    -   variability or uncertainty of sub-volume grade estimation or        interpolation;    -   or any combination thereof.

In still another embodiment of any of the method or methods describedabove, the drill plan may aim to sample sub-volumes of the one or moretarget volumes such that a highest aggregate desirability is achieved.

In yet another embodiment of any of the method or methods describedabove, the aggregate desirability may primarily consider the value ofresource classification, the decrease in estimation uncertainty, theidentification of geological features bounding the one or more targetvolumes, or a combination thereof.

In another embodiment of any of the method or methods described above,the drill plan may be constrained by a budget which limits aggregatedrill hole length.

In yet another embodiment of any of the method or methods describedabove, the drill plan may be iteratively improved by improving theglobal distribution of drill holes within the drill plan based on newlyacquired information as drilling or sampling operations progress.

In a further embodiment of any of the method or methods described above,the specified or desired level may be selected from a range spanninggeological, inferred, indicated, measured resource, and probable orproven reserve.

In yet another embodiment of any of the method or methods describedabove, the specified or desired level may be at least about 60%indicated (or at least about another desired % value or range suitablefor the particular application), while minimizing measuredclassification.

In still another embodiment of any of the method or methods describedabove, the sub-volumes may be blocks, and the algorithmic solver may aimto generate a drill plan which attempts to maximize the number of blockssampled by the drill holes of the drill plan while minimizing the totalplanned drill length of the drill plan.

In another embodiment of any of the method or methods described above,the algorithmic solver may aim to generate a drill plan which attemptsto maximize the number of sub-volumes sampled or classified per unit(for example, m³/m) of planned drill length.

In a further embodiment of any of the method or methods described above,the drill plan may provide a ranking for each planned drill hole whichis based on the relative value of each planned drill hole to the overalldrill plan.

In still another embodiment of any of the method or methods describedabove, one or more of the lowest ranked drill holes may be eliminatedfrom the drill plan.

In a further embodiment of any of the method or methods described above,the ranking may include a penalty for moving a drill hole of the drillplan away from a position at the one or more target volumes which iseasily drilled, or away from a position at the one or more targetvolumes at which drilling equipment is already located.

In still another embodiment of any of the method or methods describedabove, the value to the drill plan of changing one or more collarlocations while dynamically updating collaring dip and azimuth directionmay be assessed. In certain embodiments, such methods may be consideredas including a localized optimization.

In yet another embodiment of any of the method or methods describedabove, the algorithmic solver may be based on a multiple metaheuristicalgorithm comprising a genetic algorithm component, a tabu searchalgorithm, and an iterated local search algorithm.

In still another embodiment of any of the method or methods describedabove, the iteratively generated drill plans may be scored by a resourceconversion calculator, and the algorithmic solver may improve the drillplan score using one or more parameters which are changed using aconstraint modifier between iterations.

In a further embodiment of any of the method or methods described above,the constraint modifier may change or flex the specified or desiredlevel, one or more parameters selected from:

a historical drill hole locations constraint; a potential drilling setuplocation constraint; a drilling direction constraint; a drilling dipsconstraint; a drilling azimuth constraint; a drilling budget constraint;a sampling requirement constraint; a drilling setup availabilityconstraint; a constraint regarding distribution of drill holes fromsetups, a constraint regarding the total amount of surface grounddisturbance; a topographical constraint, an environmental constraint; aconstraint regarding environmental exclusion zones; a geological faultconstraint; a geological contacts constraint; a geological structureconstraint; a constraint regarding existing underground workings oroperations; or any combination thereof,

or a combination thereof, between iterations.

In still another embodiment of any of the method or methods describedabove, the scoring of the drill plans may include rewarding drill planswhich provide information about the location of geological structuresand contacts of the one or more target volumes, or rewarding drill holeswhich have a reasonable probability of success.

In yet another embodiment of any of the method or methods describedabove, the method may be an iterative method which is repeated usinginput which is based on newly acquired information obtained fromdrilling one or more planned drill holes from a previously generateddrill plan.

In still another embodiment of any of the method or methods describedabove, the one or more planned drill holes from the previously generateddrill plan may be drill holes which have been drilled quickly but withreduced regard for precision for geological drilling or bounding of theone or more target volumes, allowing in-fill planning, and improving thedrill plan with less invested time.

In a further embodiment of any of the method or methods described above,the orientation of a drill hole of the drill plan may be recalculated inreal-time to accommodate for on-site drilling limitations.

In still another embodiment of any of the method or methods describedabove, the on-site drilling limitations may be at least one of drillsite accessibility, drill hole geometry, drill hole timing limitations,a requirement for movement of a drill rig, setup availability, or anycombination thereof.

In yet another embodiment of any of the method or methods describedabove, the drill plan may be a dynamic solution which can berecalculated as drill hole information is acquired.

In a further embodiment of any of the method or methods described above,a completion constraint may be used to identify a point at whichsufficient drilling has been completed.

In still another embodiment of any of the method or methods describedabove, the point at which sufficient drilling has been completed may bea point at which further increase in drill hole density or lengthprovides additional value which is below a specified threshold.

In a further embodiment of any of the method or methods described above,the method may further comprise:

-   -   using implicit modeling to model geological structures,        contacts, faults, shells, surfaces, or a combination thereof, of        the one or more target volumes; and    -   updating the implicit modeling as sampling data is acquired,

thereby dynamically identifying high-value sub-volumes to be convertedfrom unclassified to geological, inferred, indicated, or measured.

In still another embodiment of any of the method or methods describedabove, the drill plan may be recalculated, and the scoring of theresulting drill plan may include a reward for solutions which allow forconversion of the identified high-value sub-volumes from unclassified toinferred, indicated or measured.

In yet another embodiment of any of the method or methods describedabove, the drill plan may attempt to minimize the total length of drillholes that are used for conversion of the one or more target volumesfrom unclassified to geological, inferred, indicated, or measured.

In another embodiment of any of the method or methods described above,information obtained from measurement while drilling (MWD) apparatus maybe used by the algorithmic solver when generating the drill plan.

In another embodiment of any of the method or methods described above,drill holes of the drill plan may be ranked based on their value to thedrill plan, and this ranking may be used to indicate which holes of thedrill plan should be drilled first.

In another embodiment of any of the method or methods described above,the method may further comprise:

-   -   using implicit modeling to model geological structures,        contacts, faults, shells, surfaces, or a combination thereof, of        the one or more target volumes; and    -   improving the model based on data acquired from carrying out the        drill plan.

In yet another embodiment, there is provided herein a computer systemfor improving mineral resource exploration and resource classificationefficiency by generation and improvement of a drill plan, said computersystem comprising:

-   -   a memory for storing program instructions; and    -   a processor for executing the program instructions;

wherein the program instructions comprise instructions for:

-   -   defining one or more target volumes of interest in 3D space;    -   segmenting the one or more target volumes into sub-volumes to        which one or more attributes indicating relative desirability        may be assigned; and    -   using an algorithmic solver to iteratively or programmatically        generate and improve a drill plan which aims to provide an        optimal or near optimal solution for drill hole distribution        within the one or more target volumes such that all or nearly        all the sub-volumes, or at least the sub-volumes of greatest        desirability, of the one or more target volumes are sampled to a        specified or desired level;

wherein the resulting drill plan comprises a collection of one or moreplanned drill holes which are defined in 3D space, and

wherein a set of operational constraints constrains the iteratively orprogrammatically generated and improved drill plan; and

wherein the computer system comprises an interface for input ofuser-defined target volume of interest parameters, operationalconstraint parameters, or a combination thereof.

In a further embodiment of a computer system as described above, theprogram instructions may comprise instructions for carrying out any ofmethod or methods as described above.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 shows a simplified example of a non-limiting embodiment of amethod for improving mineral resource exploration and resourceestimation or classification efficiency. In this example, 6 operationalconstraints are considered, three of which have been designated asvariable, and 3 of which have been designated as non-variable;

FIG. 2 shows another example of a non-limiting embodiment of a method asdescribed herein. In the illustrated method embodiment, said methodcomprises a geological targeting model and an algorithmic drill holeplacement component.

FIG. 3 shows another example of a non-limiting embodiment of a method asdescribed herein. The illustrated embodiment is related to that shown inFIG. 2, and provides a more specific and detailed example for furtherillustrative purposes;

FIG. 4 shows a further detailed non-limiting example of data flow in thegeological targeting model as shown in FIG. 3. This figure provides anillustration of data flow modeling geological decision making to providedata to the algorithmic drill hole placement component. The illustrateddata model may be used to codify selected explicit and implicit factorsin planning drill holes;

FIG. 5 shows an illustrative example of how transformation elements maybe used to programmatically determine drill hole deviation parametersfrom existing Surveyed Drill Holes;

FIG. 6 shows an example (top view) of a target volume which has beensegmented into sub-volumes and where gains have been associated to eachsub volume. In this Figure, gains are calculated as a function ofdistance from existing drillholes and distance from existing sub-volumesthat are classified as measured;

FIG. 7 shows a graph generated as part of an example in which acomparison of multiple projects or drilling configurations wasperformed. From left to right, the graph in FIG. 7 transitions betweenlinear, curved and asymptotic segments. Each inflection point maydemonstrate a drilling strategy change point where it may be decidedwhether to move forward or not. This Figure also illustrates an exampleof preferential targeting;

FIG. 8 shows an illustrative demonstration where collar locations havebeen moved back along a tunnel by 10 m. The dots show the pierce pointlocations on the tunnel's surface;

FIG. 9 shows histograms demonstrating an example in which there islittle change in the spacing within the volume of interest given thechange in collar location, demonstrating that drill hole density usingadaptive targeting as described herein does not decrease despite a 10 mchange in collaring location in this example;

FIG. 10 shows a histogram showing comparison of both a manual plan and aplan generated using methods as described herein. The bins are valueproduced by hole. The height of each bar indicates how many holes, of agiven value, are included in the drill plans;

FIG. 11 shows a histogram showing comparison of both a manual plan and aplan generated using methods as described herein. When drilling begins,how each of the drill holes ranks compared to the expected distributioncan be determined. 6 holes drilled in the drill plan are shown;

FIG. 12 shows a bubble flow diagram representing an embodiment of amethod as described herein in which examples of physical geologicalfeatures, decision criteria, and constraints which may be used orconsidered in creating a drill plan are depicted;

FIG. 13 shows graphical representations of summaries of starting and newlayouts, including information regarding the number of drill holes, thetype of drill holes, in target versus out of target data, and the drilllength, as described in non-limiting illustrative Example 3;

FIG. 14 shows a graphical representation of predicted characteristics ofdrill plan solutions generated either manually or using a method asdescribed herein, as described in Example 6. Characteristics relating tothe non-limiting illustrative example described in Example 6, includingpredicted drilling time, total drill hole length, total number of drillholes, and % coverage, are indicated;

FIG. 15 shows a graphical representation of performance indicatorsrelated to the amount of aggregate meters drilled in the target volumevs. aggregate meters to access the target volume, as described inExample 6; and

FIG. 16 shows a data flow diagram corresponding to the method used togenerate drill plan solutions of Example 6.

DETAILED DESCRIPTION

Described herein are methods and systems for improving natural resourceexploration and classification. It will be appreciated that embodimentsand examples herein are provided for illustrative purposes intended forthose skilled in the art, and are not meant to be limiting in any way,regardless of whether or not they are specifically designated asnon-limiting.

In an embodiment, there is provided herein a method for improvingmineral resource exploration and resource classification efficiency,said method comprising:

-   -   defining one or more target volumes of interest in 3D space;    -   segmenting the one or more target volumes into sub-volumes to        which one or more attributes indicating relative desirability        may be assigned; and    -   using an algorithmic solver to iteratively or programmatically        generate and improve a drill plan which aims to provide an        optimal or near optimal solution for drill hole distribution        within the one or more target volumes such that all or nearly        all of the sub-volumes, or at least the sub-volumes of greatest        desirability, of the one or more target volumes are sampled to a        specified or desired level;

wherein the resulting drill plan comprises a collection of one or moreplanned drill holes which are defined in 3D space; and

wherein a set of operational constraints constrains the iteratively orprogrammatically generated and improved drill plan.

As will be understood, a target volume of interest may be any suitablegeological volume of earth to be investigated. The target volume ofinterest may contain, or be suspected of containing, one or more naturalresources, or one or more geological structures or features of interest,or any combination thereof. By way of non-limiting example, a targetvolume of interest may be a volume of the earth located at, or adjacentto, a proposed or existing underground mining site.

It will be recognized that the target volume may be segmented or dividedinto sub-volumes. These sub-volumes may be the same as, or similar to,one another in terms of size and shape, or they may vary. Sub-volumesmay be, for example, in the form of single or groups of blocks,polygons, or other suitable shapes. In certain non-limiting embodiments,the sub-volumes may form a block model, which may provide a geometricsupport for storing information, or attributes such as is described infurther detail below, and my facilitate estimating the geological volumeand associated features.

Attributes indicating relative desirability may be assigned orassociated with individual sub-volumes or groups of sub-volumes. Theseattributes may include, by way of non-limiting example, one or more ofdistance of the sub-volume from an existing or planned drill hole;estimate variance; grade estimates; mining development or productionschedules and timing; bounding of the sub-volume by site-specificgeological structures or contacts such as, but not limited to, faults,estimated grade, rock type, etc.; and/or variability or uncertainty ofgrade estimation or interpolation of the sub-volume (i.e. a Kriging-typevariability or uncertainty value in examples where a Kriging method or aconditional simulation or other appropriate estimation method is used toestimate or interpolate spread of observed or measured values fromsamples and observations to the target volume). By way of illustrativeexample, a sub-volume such as a block within the target volume which isnot easily accessible and suspected of containing a natural resource,but has a large distance from the nearest existing and planned drillholes, and a large variability associated with its grade estimation, maybe considered a desirable sub-volume based on its assigned attributes.By way of another illustrative example, a sub-volume within the targetvolume which is adjacent or intersecting with a geological structure ofinterest may be considered a desirable sub-volume based on its assignedattributes, as sampling of this sub-volume may provide more informationregarding the geological structure. In certain examples, one or more ofthe assigned attributes may be used to determine a desirability value,or gain, of the sub-volume. Sub-volumes having the best desirabilityvalue, or gain, may be considered as desirable targets for exploratorydrill hole investigation.

In certain embodiments, the method may further include the use of one ormore constraints which limit or constrain the algorithmic solver and/orthe resulting drill plan(s) to those meeting certain requirements orlimitations. By way of example, constraints may include any of a varietyof suitable operational constraints. By way of example, suitableoperational constraints may include at least one of a historical drillhole locations constraint, a potential drilling setup locationconstraint, a drilling direction constraint, a drilling dips constraint,a drilling azimuth constraint, a drilling budget constraint, a samplingrequirement constraint, a drilling setup availability constraint, aconstraint regarding the total amount of surface ground disturbance, atopographical constraint, an environmental constraint, a constraintregarding environmental exclusion zones, a geological fault constraint,a geological contacts constraint, a geological structure constraint, ora constraint regarding existing underground workings or operations, orany combination thereof. By way of example, the method may include useof operational constraints comprising a potential drilling setuplocation constraint, a drilling dip constraint, and a drilling azimuthconstraint to limit or constrain the algorithmic solver and/or theresulting drill plan(s). Operational constraints for a particulardrilling site may include constraints on the allowable drilling startingpoints (collars), and/or the upper, lower, and outer perimeter drillinglimits. Geological operational constraints may include examples such asa geological fault constraint, a geological contacts constraint, or ageological structure constraint. In certain embodiments, the operationalconstraints may include a drilling location constraint which restrictsthe algorithmic solver to only drill plans having drill holes located ata specified set of positions at the one or more target volumes. In anembodiment, the operational constraints may include one or moreenvironmental constraints such as a constraint regarding the totalamount of surface ground disturbance; such an operational constraint maybe desirable in examples where environmental licensing limits where andhow much ground may be disturbed. In a further embodiment, theoperational constraints may include: topographical constraints whichaccount for, for example, steep mountain sides or other such features;constraints regarding environmental exclusion zones; and/or constraintsregarding existing underground workings or operations. In certainembodiments, the operational constraints may include a constraint basedon the angle with which the proposed drill holes intersect any of thegeological structures and/or contacts.

By way of non-limiting example, examples of constraints may include aspecification of allowable drill hole start positions, such as startpositions which are easily accessible or at which a drill rig is alreadylocated or nearby. In such a non-limiting example, which is provided forillustrative purposes to the person of skill in the art, the resultingdrill plan may provide 3 dimensional specifications of one or more drillholes to be drilled from each of the specified start positions whichprovide an optimized sampling of the target volume within the specifiedconstraints.

The person of skill in the art will be aware of algorithm typesgenerally applicable as algorithmic solvers for solving problems (i.e.generating drill plans) such as those problems described herein. By wayof non-limiting example, the algorithmic solver may be a linearalgorithm, a heuristic algorithm, a metaheuristic algorithm, ametaheuristic SCP algorithm, a localized random search; a modifiedrandom search; a taboo search; or an annealing algorithm. Examples ofsuitable algorithmic solver types may be those described in, forexample, Metaheuristiques hybrids pour les prob/emes de recouvrement etrecouvrement partie/ d'ensemb/es appliquús au problem de positionnementdes trous de forage dans les mines (Nehme Bilal, Thesis, EcolePolytechnique, Montreal, Canada, 2014; ISBN 9781321840629, 1321840624),which is herein incorporated by reference in its entirety. The person ofskill in the art having regard to the teachings herein will recognizethat a variety of different algorithms may be possible, such as forexample a simulated annealing-based algorithm (see, for example,Soltani-Mohammadi and Hezarkhani, Natural Resources Research, Vol.22(3), pages 229-237, 2013, which is herein incorporated by reference inits entirety). In a further non-limiting embodiment, the algorithm maybe a linear method such as that of the CPLEX optimization softwarepackage, for example.

In certain embodiments, a metaheuristic algorithm such as that describedin Metaheuristiques hybrids pour les prob/emes de recouvrement etrecouvrement partiel d'ensemb/es appliqués au problem de positionnementdes trous de forage dans les mines (Nehme Bilal, Thesis, EcolePolytechnique, Montreal, Canada, 2014; ISBN 9781321840629, 1321840624)may be used in an algorithmic solver. Such an algorithmic solver maycomprise a metaheuristic algorithm comprising a genetic algorithmcomponent, an iterated local search algorithm, and a tabu searchalgorithm, for example.

In an embodiment the algorithmic solver may involve an iterative orprogrammatic algorithmic approach which attempts to solve the problem ofdrill hole number and placement at the target site (i.e. attempts togenerate and improve a drill plan which aims to provide an optimal ornear optimal solution for drill hole distribution within the one or moretarget volumes such that all or nearly all the sub-volumes, or at leastthe sub-volumes of greatest desirability, of the one or more targetvolumes are sampled to a specified or desired level). In certainembodiments, the algorithmic solver may attempt to solve surface-basedlayout problems, underground-based layout problems, or both. A drillplan may be a plan which outlines a proposed or predicted collection ofdrill holes to be used for sampling or investigating the target site.The drill plan may define the drill holes of the plan in terms of their3D position in any suitable manner. The drill plan may provide3-dimensional characteristics of each drill hole such that each drillhole can be understood, and drilled, by those of skill in the art. Thedrill plan may, for example, provide the start position of each drillhole, the depth of each drill hole, and the path the drill hole is tofollow, or may follow, from the start position through or into thetarget volume. Characteristics such as collar location, dip, azimuth,and/or drill hole length may be provided as desired or needed. In anembodiment, the drill plan may also use a projected (or estimated)description of drill hole deviation to determine the expected (althoughnon-prescriptive) path that the drill hole may follow when drilled. Inanother embodiment, the drill plan may include parameters relating tovariability tolerances of the drill holes in the drill plan.

The algorithmic solver aims to generate and improve a solution, in theform of a drill plan, which provides an optimal or near optimal solutionfor drill hole distribution within the one or more target volumes suchthat all or nearly all the sub-volumes, or at least the sub-volumes ofgreatest desirability, of the one or more target volumes are sampled toa specified or desired level. It will be understood that generation andoptimization or improvement of the drill plan may be an iterative,dynamic process. As additional information is acquired about the targetvolume as a result of exploratory activities such as drilling one ormore planned drill holes from a previously or currently generated drillplan, the drill plan can be updated. For example, the one or moreattributes assigned to the sub-volumes of the target volume can beupdated as additional information is obtained, which may alter the drillplan solution produced by the algorithmic solver such that the new drillplan provides a more optimal solution which takes into account thenewest available information and may, for example, provide an improvedglobal distribution of drill holes within the drill plan. By way ofexample, the drill plan may be iteratively improved by improving theglobal distribution of drill holes within the drill plan based on newlyacquired information as drill operations progress, based on newinformation obtained on-site (such as, for example, collar locationlimitations, etc . . .), or any combination thereof.

In certain embodiments, one or more planned drill holes from apreviously generated drill plan may be drill holes which have beendrilled quickly but with reduced regard for precision forgeological-type drilling or bounding of the one or more target volumes.Accuracy generally decreases with speed. The information acquired fromthese one or more quickly drilled drill holes may be used to improve thedrill plan with less invested time, and may allow for in-fill planning.Quickly drilled initial holes may have lower precision of targeting,however these drill holes may provide information about boundariescontaining mineralization, for example. In-fill drilling would thenallow drilling of the remaining budget (for example, two-thirds if onethird was quickly drilled) to increase overall classification andconversion. Such an approach may employ single drill hole adaptabilityand local optimization within a drill plan as described herein, as wellas a means for adapting the location of subsequent drill holes giveninitial drill hole locations that were drilled quickly, as alsodescribed herein.

It will be understood that the iterative and dynamic nature of methodsas described herein may in an embodiment allow for the orientation of adrill hole of the drill plan to be recalculated in real-time toaccommodate for on-site drilling limitations which may be encountered.Such on-site drilling limitations may include drill site accessibility,drill hole geometry limitations, drill site timing limitations, arequirement for movement of a drill rig, setup availability,environmental limitations, availability, performance limitations of thedrill, drill availability, or any combination thereof.

In certain embodiments of the methods and systems described herein, itwill be understood that a user may input, contribute, modify, or removeone or more drill holes to or from the drill plan which isgenerated/improved by the algorithmic solver. Such method and systemembodiments may be considered as combined manual/automated methods andsystems. In certain embodiments, the user may be provided withinteractive feedback on the one or more contributed, modified, orremoved drill holes with respect to their desirability, or with respectto the desirability of the resultant generated/improved drill plan whichincludes the one or more user-contributed, modified, or removed drillholes.

In certain embodiments, operational constraints may involve timingconsiderations. For example, changing collar location may affect thetime taken to execute a program. If drill rigs do not need to be movedas often and/or the collar setup is quicker or potentially eliminated,then there may be a net gain in the time taken to drill a drill plan.

It will be additionally understood by the person of skill in the arthaving regard to the teachings herein that Measurement While Drilling(MWD) technologies may optionally be used or considered as part of themethods described herein. In certain embodiments, information obtainedfrom measurement while drilling apparatus may be used when generating orimproving the drill plan. Measurement-while-drilling apparatus may beemployed as a further means for collecting information to be supplied tothe algorithmic solver for iteratively generating an improved drillplan. By way of non-limiting example, measurement while drillingapparatus may be used to obtain data from the drill rig which may beused to facilitate or improve interpretation of geological contacts orrock types in the target volume. Examples of MWD apparatus are describedin, for example, Canadian patent application no. 2,787,851, which isherein incorporated by reference in its entirety. MWD technologyallowing determining of rock characterization at the drilling face aswill be known to the person of skill in the art may be suitable for suchapplications.

In certain embodiments, the drill plan may aim to sample sub-volumes ofthe target volume(s) such that the highest aggregate desirability isachieved. Using such an approach may result in a drill plan which favorsdrill holes which primarily sample or investigate sub-volumes having ahigh desirability as determined by their associated attributes. Incertain non-limiting embodiments, the attributes assigned to thesub-volumes which are sampled by a given drill hole may be used todetermine a desirability value of the drill hole. Drill plans may, incertain non-limiting embodiments, aim to provide a collection of drillholes in which each drill hole has a maximized or near maximizeddesirability value, or may aim to provide a collection of drill holeshaving an overall, average, or aggregate desirability value determinedfrom the desirability values of the individual drill holes which has amaximized or near maximized value. By way of non-limiting example, theaggregate desirability may primarily consider the value of resourceclassification, the decrease in estimation uncertainty, theidentification of geological features or structures bounding the one ormore target volumes, or a combination thereof.

In certain further embodiments, the algorithmic solver and/or resultingdrill plan may be constrained by a budget constraint such that onlydrill plans meeting a specified budget constraint will be allowed orproduced. A budget constraint may include, for example, one or more ofconstraints which limit the allowable total drill hole length of thedrill plan, the drill hole length of individual drill holes, the totalnumber of setup locations, the total time or predicted time to drill theplan, the location or position of drill holes, the collar locations,and/or may limit drill holes to positions which are more easilyaccessible, for example. A budget constraint may limit, for example,aggregate drill hole length of the drill plan. In an embodiment, thealgorithmic solver may aim to generate a drill plan which attempts tomaximize the number of sub-volumes sampled or classified by the drillholes of the drill plan while minimizing the total planned drill lengthof the drill plan, or while attempting to maximize the number ofsub-volumes sampled or classified per unit of planned drill length(i.e., per meter, or per foot).

In natural resource exploration, target volumes are often discussed interms of value, or reportable value. The person of skill in the art willbe aware of suitable criteria which may be used to classify sub-volumesor a target volume, or a target volume itself. By way of non-limitingexample, NI-43-101 specifies standards of disclosure for mineralprojects in Canada, JORC in Australia. A qualified person generallyprovides classification criteria used to calculate/classify a resource.

In an embodiment of a method as described herein, the iteratively orprogrammatically generated and improved drill plan(s) may be scored by aresource conversion calculator, and the algorithmic solver may improvethe drill plan score using one or more parameters (such as, for example,operational constraints as described above, the specified or desiredlevel as described herein, or a combination thereof) which are changedor flexed using a constraint modifier between iterations. In certainembodiments, scoring of the drill plan(s) may include rewarding drillplans which provide information about the location of geologicalstructures and contacts of the one or more target volumes, or rewardingdrill plans which have a reasonable probability of success, or both.

Exploratory sampling of a target volume enables classification ofsub-volumes of the target volume. A method as described herein mayprovide a drill plan which aims to provide an optimal or near optimalsolution for drill hole distribution within the one or more targetvolumes such that all or nearly all the sub-volumes, or at least thesub-volumes of greatest desirability, of the one or more target volumesare sampled to a specified or desired level. This specified or desiredlevel may be related to resource/sub-volume/target volumeclassification. Such classification is typically based on the extent towhich the sub-volume has been sampled by the drill holes of theexploratory sampling. By way of non-limiting example, sub-volumes may beclassified or ranked, in order of increasing confidence, asunclassified, geological, inferred, indicated, or measured resource(see, for example, Prospecting and Exploration of Mineral Deposits,Bohmer and Kucera, Elsevier, Developments in Economic Geology (21),2^(nd) Ed., 1986, which is herein incorporated by reference in itsentirety). In an embodiment, the specified or desired level may beselected from a range spanning geological, inferred, indicated, measuredresource, and probable or proven reserve. By way of non-limitingexample, the specified or desired level may be at least about 60% (or atleast about another desired % value suitable for the particularapplication) of the target volume should be converted to indicated whileminimizing measured.

It will be understood that mineral resource upgrading may refer to theadditional sampling (in most cases, drilling) that is performed tocreate sufficient confidence to all the shape, extents, continuity, andgrade of a volume of ground to be estimated. Deposit upgrading may referto industry accepted, and in some jurisdictions mandated, definitionsrelated to the quality of estimation for a geological volume of ground.

As the density of sampling increases, the risk associated to theestimation of a deposit's shape, location, continuity, and mineralcontent decreases. A sampling criteria suitable for categorizing avolume of ground from geological, to inferred resource, indicatedresource, and measured resource, may be proposed. Additional engineeringevaluation and sampling may then allow for a resource volume to be movedinto probable and proven reserve categories.

Although measured classification is among the highest level ofconfidence, it should be noted here that in certain non-limitingexamples converting too much of a target volume to measuredclassification may be seen as overdrilling or oversampling for thecurrent stage of exploration; in such examples, a certain volume of thetarget being classified at a lower level such as indicated or inferredmay be sufficient.

In yet another embodiment, the resulting drill plan may provide aranking or scoring for each planned drill hole which is based on therelative value of each planned drill hole to the overall drill plan. Theoverall value may be based on, for example, the aggregate relativedesirability of the sub-volumes sampled by the drill hole. In certainembodiments, one or more of the lowest ranked drill holes may beeliminated from the drill plan to improve the overall efficiency of theplan.

In still another embodiment, a ranking or scoring of the drill plan mayinclude a penalty for moving or repositioning a drill hole of the drillplan away from a position at the one or more target volumes which iseasily drilled, or away from a position at the one or more targetvolumes at which drilling equipment is already located. As well, oralternatively, ranking or scoring of the resulting drill plan(s) mayinclude rewarding drill plans which provide information about thelocation of a geological contact or structure which is between differentdeposit types or two rock types in the target volume, or rewarding drillplans which have a reasonable probability of success (which may berelated to, for example, the probability a drill hole can be completed).As well, or alternatively, scoring of the drill plans may include areward for drill plans which allow for conversion of the identifiedhigh-value sub-volumes from unclassified to inferred, indicated, ormeasured, optionally using a minimized aggregate drill hole length orminimized number of drill holes. In a further embodiment, the value tothe drill plan of changing one or more drill hole collar locations whiledynamically updating dip and dip direction may be assessed.

In certain embodiments of a method as described herein, a completionconstraint may be used to identify a point at which sufficient drillinghas been completed. It will be understood by the person of skill in theart that after a sufficient amount of sampling has been completed,further sampling may provide diminishing returns in terms of the valueand breadth of additional information acquired compared to the costassociated with acquiring that information. Thus, in certainembodiments, a constraint may be set to flag the point at which furtherdrilling may not be considered being worthwhile. The point at whichsufficient drilling has been completed may be a point at which furtherincrease in drill hole density may provide additional value which isbelow a specified threshold (i.e. may not be worthwhile)(i.e. by way ofnon-limiting example, about 60% of target volume to indicatedclassification).

In still another embodiment of a method as described herein, the methodmay further comprise:

-   -   using implicit modeling to model geological structures,        contacts, faults, shells, surfaces, or a combination thereof, of        the one or more target volumes; and    -   updating the implicit modeling as drill hole data is acquired,

thereby dynamically identifying high-value sub-volumes to be convertedfrom unclassified to geological, inferred, indicated, or measured.

In still another embodiment of a method as described herein, the methodmay further comprise:

-   -   using implicit modeling to model geological structures,        contacts, faults, shells, surfaces, or a combination thereof, of        the one or more target volumes; and    -   improving the model based on data acquired from carrying out the        drill plan.

In still another embodiment, drill holes of the drill plan may betargeted to improve the implicitly modeled surfaces.

Examples suitable for implicit modeling may include GoCAD, MAPTEK,LEAPFROG for implicitly producing surfaces.

Suitable methods and techniques for implicit modeling will be known tothe person of skill in the art having regard to the teachings herein.Generally, implicit modelling may be used to create 3D models includingsurface shells which represent volumes which are being investigated bydrilling. Grade contours, lithological contacts, potential faultinterpretations, and/or other features may be represented. Implicitlymodeled surfaces may, for example, be updated as new drilling occurs,allowing updating of volumes which are to be drilled at a given densityto provide sufficient sample information to achieve resourceclassification criteria.

It will be understood that in certain embodiments, the drill plan may berecalculated, and the scoring of the resulting drill plan may include areward for solutions which allow for conversion of the identifiedhigh-value sub-volumes from unidentified unclassified to inferred,indicated or measured. The drill plan may attempt, for example, tominimize the total length of drill holes that are used for conversion ofthe one or more target volumes from unclassified to indicated, inferred,or measured. The drill plan may change setup locations.

In certain embodiments, modelling, algorithmic solvers, and/or drillplans as described herein may attempt to account for drill holedeviations which may occur, such as a change in loft, or a change incurl. Suitable methods for predicting drill hole deviations will beknown to the person of skill in the art having regard to the teachingsherein.

It will be understood by the person of skill in the art having regard tothe teachings herein that methods described herein may allow foriterative recalculations to generate and improve resulting drill plans.In certain non-limiting embodiments, the orientation/position of a givendrill hole with respect to the target can be updated or recalculated inreal-time at the drill site, allowing real-world limitations orconditions to be accounted or adjusted for in the drill plan asexploratory drilling proceeds. Such embodiments may, in certainexamples, reduce the amount of time invested in precisely placing thedrill and precisely drilling drill holes to match rigid drill plansgenerated manually off-site, as has been conventionally done in thefield.

In another embodiment, a non-limiting example of a method for improvingmineral resource exploration and resource classification efficiency asdescribed herein may comprise:

-   -   modelling the problem of drill holes number and placement at a        target volume as a Set Covering Problem (SCP); and    -   solving the SCP using an algorithmic solver, and scoring the        resulting solutions to the SCP, thereby identifying a solution        to the SCP which optimizes the distribution of the drill holes        at the target volume;

wherein the solution to the SCP attempts to maximize the volume of thetarget volume which can be converted from unclassified to inferred,indicated or measured classification while remaining within a set ofoperational constraints.

The person of skill in the art will recognize that the SCP, and thealgorithmic solver, may be or comprise an SCP and a metaheurisiticalgorithm such as that described in Metaheuristiques hybrids pour lesprob/emes de recouvrement et recouvrement partiel d'ensemb/es appliquésau problem de positionnement des trous de forage dans les mines (NehmeBilal, Thesis, Ecole Polytechnique, Montreal, Canada, 2014; ISBN9781321840629, 1321840624).

In a further embodiment, such a method may use an iterative process todetermine drill hole spacing and coverage parameters for the modelledSCP. The person of skill in the art will further recognize thatsolutions to the SCP may be in the form of a drill plan, which outlinesa proposed or predicted collection of drill holes to be used forsampling or investigating the exploration site as described above. Incertain embodiments, the solution to the SCP (i.e. the drill plan) mayprovide a ranking for each planned drill hole of the solution, which isbased on the relative value of each planned drill hole to the overallsolution. One or more of the lowest ranked, or lowest relative valued,drill holes may, optionally, be eliminated from the solution to the SCPas desired.

It will be understood to the person of skill in the art having regard tothe teachings herein that the set of operational constraints maycomprise any of a variety of suitable operational constraints such asthose described above.

In certain embodiments, the modelled SCP of the method may divide theexploration site into blocks or sub-volumes within a target volume, andthe solution to the SCP may attempt to maximize or improve the number ofblocks or sub-volumes sampled by a collection of drill holes while alsominimizing, reducing, or capping the total number of drill holes and/orthe total drill hole length in the collection. In certain additionalembodiments, the modelled SCP may, for example, divide the explorationsite into blocks or other sub-volumes within a target volume, and thesolution to the SCP, such as a drill plan as described above, mayattempt to maximize the volume of the exploration site which can beconverted to a specified classification which is selected from a rangespanning inferred, indicated, or measured resource, probable or provenreserve. In certain examples of a method as described herein, scoring orranking of the resulting solutions to the SCP (i.e. scoring of drillplan solutions) may be performed as described above.

It will be understood by the person of skill in the art having regard tothe teachings herein that the methods as described herein may beiterative methods which may be repeated by re-modelling the problem ofdrill holes number and placement based on newly acquired informationabout the exploration site obtained from drilling and sampling a hole.In certain embodiments, the orientation of a drill hole of the solutionto the SCP may be recalculated in real-time to accommodate for on-sitedrilling limitations such as those described above.

It will be understood by the person of skill in the art having regard tothe teachings herein that methods as described herein may, in certainnon-limiting embodiments, include consideration of factors, components,constraints, and/or parameters including, but not limited to, any one ormore of the following:

-   -   Resource Sensitivity: Given a resource classification criteria,        it may be determined how conversion is affected by flexing        constraints in the geological targeting model. Given the        geometric complexity of most drilling programs, a large number        of constraints may be modified without changing the spacing        requirement. The drill hole spacing requirement may be geo        statistically determined, and changing its value may inherently        change the value of the sampling program.    -   Classification/Deviation Sensitivity: The use of drill hole        deviation models may provide an estimate of the change of drill        hole direction; it is presumed, however, that the actual drill        hole pierce point in a target will diverge from the estimated        location. This difference between the plan and the actual pierce        point may affect the overall expected classification. With a        Monte Carlo type simulation in conjunction with a resource        conversion calculator, it may be possible to determine a        confidence interval on the expected classification conversion        given an expected overall drilling accuracy estimate.    -   Drill hole Spacing (in 2D or 3D): Resource conversion may be        defined by the amount of sampling required to reduce        geo-statistical uncertainty below a defined threshold.        Geometrical complexity often makes it difficult to space drill        holes correctly or optimally on a regular grid. A typical        simplification may be to define a spacing of holes, often        represented as a grid spacing in 2D. This grid is then projected        into 3D space. This nominal or targeted spacing defines the        density of drilling that may be required to potentially move a        volume of ground from one resource category to the next.    -   Budget: The total cost of the drilling campaign; depending on        the location, this may include direct drilling costs or may        include all costs associated to drilling the campaign and, in        some cases, analysing the results.    -   Full drilling budgets: The full budget may be determined as the        least amount of drilling required to classify the full target        volume into the specified resource category. Because of        decreasing returns, the full budget may rarely, if ever, be        drilled. The full budget provides an upper budgetary limit for        excellent conversion.    -   0-100 (full) % Drilling Budget: Determining the full budget        required to transfer all of the target volume(s) of interest to        target higher resource classification may create an upper        budgetary boundary. Varying the budget from no drilling to the        maximum budget highlights a number of dynamics related to the        volume of interest and the classification scheme, which may        allow decisions to be made based on quantified values.    -   Multiple Solutions/Drill Plans: The resource conversion dynamics        are such that for relatively small budgets there may be a large        number of equi-valuable or near equi-valuable solutions. An        extreme example is the value that a single drillhole can        produce—given a single hole there are a variety of locations        where it can be drilled. When considered within a larger        context, the single hole may have the same overall conversion        but other metrics may make one hole better than another.    -   Planned Hole Ranking (i.e. assigning a relative rank or value to        drill holes): Given a solution drill plan made up of a number of        drill holes, the relative value produced by each of the drill        holes, for either exploration or resource conversion, may not be        identical. By providing a measure of value anticipated/produced        by each of the drill holes, it may be possible to rank the order        in which holes should be drilled. For a given setup location, in        particular when under tight time constraints, the highest value        holes may be drilled first (see, for example, FIG. 11).    -   Setup Locations: May define where drill rigs may be located to        undertake drilling holes. In surface drilling, environmental        constraints may limit where drills can be setup. Because        underground environments generally involve excavated tunnels to        position the drills, the cost of setups may be a significant        budgetary allocation within the overall drilling budget.    -   Setup Timing/Availability: Setup locations may not always be        available during the full duration of a drilling campaign.        Maximizing when drilling can occur at a location, that is only        available for a short period of time, may improve the overall        effectiveness of a drilling program.    -   Drilling Setup Ranking: Each setup may be ranked by the value        that it provides to the overall drilling campaign. When a number        of setups are combined, it may be possible to calculate the        relative value that setup placement may have on the overall        program's efficiency.    -   Drilling Constraints: depending on the geometry of the setup        location and on the type of drill rig being selected, the        orientation of the drilling may be constrained. Including these        constraints may allow that for each setup, planned holes may be        effectively drilled. Typically, constraints are defined as the        dip and azimuth ranges available from each drilling location.    -   Resource conversion efficiency: Resource conversion efficiency        may provide an unambiguous means of comparing possible drill        plans. Using metres drilled by meter cube converted (m/m³)        provides an objective means of gauging the effectiveness of        drill programs.    -   Drill hole deviation modeling: Drilling and in-situ rock        property variations may deflect the path of the drill string as        drilling progresses. A deviation in both loft (upward        inclination) and curl (spiraling) may be applied to predict the        expected path for the drillholes. Analysis of historical        drilling data may be used to guide the determination of the        appropriate parameters to use to predict the deviation.    -   Target volume or Volume of interest (in 2D or 3D): The volume of        interest or target volume may define a volume of ground that is        to be sampled. The expectation is that sampling may be        undertaken by drilling. The volume of interest need not be        contiguous. Historically, the 3D volume of interest was        segmented into a series of 2D planes with drilling being        constrained within these planes.    -   Drill Hole Geometric Constraints: The drill setups and the        selection of drill rig may deterimine physical limits that may        be applied to each drill plan. Geometric constraints are often        set by historical experience (i.e. underground fan drilling        patterns—zero change in azimuth while varying dip—to determine        true mineralization width).    -   Faults, Structures, Contacts: The geological domain under        exploration may not be homogeneous and is often intersected by        structures and changes in lithology, mineralogy and alteration        (if it were homogeneous, there would be little value in        additional exploration). Determining the location of these        geological discontinuities and features (extension from existing        volume), is a component of exploration, as these features        typically constrain the location where mineralization is        expected.    -   Drill hole to structure constraints (angle of incidence): may        determine the limits associated to drilling through contacts or        boundaries. This may include ensuring that geological targets        are hit at specific angles in order to determine “true width”,        or to ensure that faults are hit at angles that decrease the        chance that the drill hole will deviate along the structure.    -   No Drill Volumes (Exclusions): Volumes of ground where drill        holes cannot be drilled. An example of this is where historical        mining exists, and drilling into historical openings would        jeopardize the drill hole.    -   Setup Exclusion Volumes: The location where drill rigs may be        setup may be limited. On surface, this is often due to        topographic or environmental exclusions. In the underground        environment, opening geometries, production, travel traffic        patterns, and the availability of services often limit the        locations where drill rigs may be setup.    -   Classification criteria: Based on experience, and backed by        geostatistical modeling of historical samples, the        classification criteria may describe non-prescriptive criteria        to determine when a sub-volume of ground can be moved to higher        value classification. Inherent in the classification criteria is        the concept that the classification is being performed within a        certain confidence interval—increasing resource classification        from inferred to measured reduces risk but does not eliminate        it.

Classification Targets: Because it may be economically difficult tojustify that 100% of volume of interest be upgraded to the higherresource classification category, it is common practice that a targetpercentage be defined. Thus, the target for a drill program may be toreach, for example, 80% conversion to the Indicated category.

-   -   Resource Classification Trade-Offs: While drilling to upgrade to        the Indicated category, for example, it may be possible that        some of the volume of interest be upgraded to Measured. This        upgrading may be un-desired in certain examples, and may be an        indication of over-drilling. As part of the optimization,        limiting the amount of ground that is “over converted” may be        done to limit over-drilling. Alternatively, varying the        percentage of ground converted between Inferred and Indicated        may be an effective means of maximizing the value of a drilling        program by both de-risking the ground (Indicated), and increased        the projected size of the mineralized volume (Inferred).    -   Block properties: The volume of interest may be subdivided into        a number of smaller sub-cells or sub-volumes. The cell volumes        may be constant or variable volume within the volume of        interest. Quantifiable properties, such as estimated grades,        estimated variance, distance to nearest drillhole, samples        within the search ellipsoid, etc . . . , may be allocated to        each of the cells. Adding quantification to the sub-volumes        within the volume of interest may be done by placing the values        at the centroid of each of the sub-cells, for example.    -   Targeting Gains: determining drilling targeting priorities may        be partially accomplished by adding a desirability index to each        sub-cell within the volume of interest. A simple example of        desirability may be, for example, the use of distance to an        existing drill hole. More advanced criteria may integrate grade        estimation.    -   Distance to existing drill holes: This criteria may be used as        an initial targeting parameter, as it generally provides the        highest level of gain for a limited budget.    -   Estimated Grade: As part of the resource estimation, the assayed        mineral content samples taken from existing drill holes may be        interpolated/extrapolated to the full volume of interest. These        estimates may then be used to determine the overall mineral        content within the volume of interest. The estimate may be        associated to an estimate error, and an overall sample variance,        which provides an indication to the quality of the estimate.    -   Geological Domains: Interpreted (soft) volumetric boundaries        used to separate ground that share common lithology, grade        continuity or alterations. The drilling requirements may be        specific to each of the geological domains. The level of        uncertainty on the domain's boundaries may decrease as drilling        density increases.    -   Plan Execution: Once a plan has been approved, drill rigs and        crews execute the work. The value of the program is generally        gauged on how closely the actual hole collar is to the planned        collar (typical requirement within 1 metre), how accurately the        hole's initial dip and azimuth are executed, and in some cases        how closely the hole tracks to the expected path. During the        execution, drilling data and core may be collected and analysed        to update the plan.    -   Mining development or production schedules and timing: Mining        development or production schedules and/or other timing        considerations may be included as part of the one or more        attributes indicating relative desirability assigned to        sub-volumes.    -   Local Drilling Constraints: Executing a plan is often times        constrained by local limitations. By way of example, in the        underground environment, the configuration of the drilling rig        may not allow all expected dip and azimuths to be drilled        correctly.    -   Local Optimization: Given the current local constraints, the        driller may have the option of selecting an alternative drill        plan and using a geological model such as that described herein        to verify the value of the change. This may be as simple as        removing a constraint that drillholes have to be collared within        a meter of the planned collar location and then updating the        dip/azimuth to hit/sample the expected target volume, for        example.

It will be understood by the person of skill in the art that the methodsas described herein are suitable for execution on a computer. Thus, inyet another embodiment, there is provided herein a computer system forimproving mineral resource exploration and resource classificationefficiency by generation and improvement of a drill plan, said computersystem comprising:

-   -   a computer-readable memory for storing program instructions        (i.e. computer executable instructions); and    -   a processor for executing the program instructions;

wherein the program instructions comprise instructions for:

-   -   defining one or more target volumes of interest in 3D space;    -   segmenting the one or more target volumes into sub-volumes to        which one or more attributes indicating relative desirability        may be assigned; and    -   using an algorithmic solver to iteratively or programmatically        generate and improve a drill plan which aims to provide an        optimal or near optimal solution for drill hole distribution        within the one or more target volumes such that all or nearly        all the sub-volumes, or at least the sub-volumes of greatest        desirability, of the one or more target volumes are sampled to a        specified or desired level;

wherein the resulting drill plan comprises a collection of one or moreplanned drill holes which are defined in 3D space, and

wherein a set of operational constraints constrains the iteratively orprogrammatically generated and improved drill plan; and

wherein the computer system comprises an interface for input ofuser-defined target volume of interest parameters, operationalconstraint parameters, or a combination thereof.

In a further embodiment of a computer system as described above, theprogram instructions may comprise instructions for carrying out any ofmethod or methods as described above. The program instructions may causea computer to perform steps of such methods when executed.

In an embodiment of a computer system as described herein, thecomputer-readable memory of the computer system may comprise anysuitable non-transitory, tangible computer-readable media such as, butnot limited to, CD, DVD, Blu-Ray, flash drive, hard drive, cloud drive,or remote storage media accessible via the internet or othercommunications channel. Computer-readable media may include, forexample, any of volatile and non-volatile, removable and non-removabletangible media implemented in any method or technology for storage ofinformation such as computer readable instructions, data structures,program modules, or other data. Computer-readable memory may include,but is not limited to, RAM, ROM, EPROM (eraseable programmable read onlymemory), EEPROM (electrically eraseable programmable read only memory),flash memory, or other memory technology, optical storage media,magnetic cassette, magnetic tape, magnetic disk storage, or othermagnetic storage media, or other type of volatile and/or non-volatilememory, and any other suitable tangible medium which may be used tostore the desired information and which can be accessed by a computerincluding and any suitable combination of the foregoing, as will beknown to the person of skill in the art having regard to the teachingsherein.

In certain embodiments, computer-readable data embodiment on one or morecomputer-readable media may define instructions, for example, as part ofone or more programs, that, as a result of being executed by a computer,instruct the computer to perform one or more of the functions describedherein. Such instructions may be written in any suitable programminglanguage known to the person of skill in the art. The computer-readablemedia on which such instructions are embodied may, in certainembodiments, reside on one or more of the components of either a systemor computer readable medium described herein, may be distributed acrossone or more of such components, and may be in transition therebetween.

The computer-readable media or memory may, in certain embodiments, betransportable such that the instructions stored thereon can be loadedinto any suitable computer resource to implement the methods asdescribed herein. In addition, it will be understood that theinstructions stored on the computer-readable media described above arenot limited to instructions embodied as part of an application programrunning on a host computer. Rather, the instructions may be embodied asany type of computer code (e.g. software or microcode) that may beemployed to program a computer to implement aspects of the methodsdescribed herein. The computer executable instructions may be written ina suitable computer language or combination of several languages.

In certain embodiments, computer systems as described herein may includeone computer, or multiple computers in communication via one or morecomputer networks, and may optionally include one or more servers,terminals, displays, user interfaces, and/or printers. In certainembodiments, one or more computers and/or user interfaces maycommunicate via, for example, a data telecommunications network, a localarea network (LAN), a wide area network (WAN), internet, intranet,extranet, and may include local and/or distributed computer processingsystems.

In certain embodiments, computer systems as described herein may includea suitable operating system as will be known to the person of skill inthe art. In certain embodiments, computer systems described herein mayinclude one or more displays for conveying information to a user, andone or more user interfaces through which the user may interact with thesystem.

It will be understood by the person of skill in the art that in certainembodiments the program instructions may be implemented in the form ofsoftware, firmware, or hardware. In certain embodiments, the programinstructions may be implement in the form or an API (applicationprogramming interface), or in the form of a RESTful (representationalstate transfer) web service, for example. The program instructions maycomprise instructions in the form of a programming language as will beknown to the person of skill in the art, and may include a userinterface allowing a user to enter parameters or selections concerning,for example but not limited to, operational constraint information,target volume information, specified or desired level information,relative desirability attribute information, algorithmic solverselection or customization information, constraint modifier information,resource conversion calculator information, drill plan information,drill plan scoring information, one or more drill holes to be includedin the drill plan, or any combination thereof. The user interface mayallow a user to review generated drill plans, adjust or flex parameterssuch as operational constraints, and trigger the generation of new drillplans. The user interface may also present the user with a generated orimproved drill plan and relevant information contained therein. Thedrill plan may be presented to a user graphically, or in any othersuitable form as desired. Drill plans may be transmitted to other userssuch as an on-site drilling team, who may also be allowed access to theuser interface and/or computer system as described herein.

The person of skill in the art will also understand that the programinstructions may be executed on one computer or on several computers. Byway of example, the algorithmic solver may run on a server or remotecomputer having a more powerful processor in order to save time inexamples where a user is accessing the system on a handheld device suchas a phone or tablet.

Examples of certain non-limiting embodiments of methods and systems asdescribed herein will now be described. It will be understood that theseexamples are provided for illustrative purposes to the person of skillin the art, and are not intended to be limiting in any way.

EXAMPLE 1—DECISION CRITERIA, CONSTRAINTS, METHODS, AND SYSTEMS FOR DRILLPLAN GENERATION AND OPTIMIZATION

A simplified example of a non-limiting embodiment of a method forimproving mineral resource exploration and resource/reserveclassification efficiency is shown in FIG. 1. In this example, 6operational constraints are considered, three of which have beendesignated as variable, and 3 of which have been designated asnon-variable. Non-variable constraints in this example are used todefine limitations and parameters which are to be met by the drill plan,and variable constraints are parameters and limitations which can bemodified, flexed, or altered during the generation and optimization ofthe drill plan. It will be understood that these variable andnon-variable designations are non-limiting and may change as desired. Inthis example, non-variable constraints include the volume of interest(i.e. a contiguous or non-contiguous volume of ground to be sampled) andthe sampling requirements (i.e. the density of drilling required toappropriately sample the volume). It will be understood that the volumeof interest is not always completely defined, and part of a drillingcampaign may focus on determining the more detailed location or boundsof the target volume. In certain examples, to simplify the problemspace, the volume of interest may be represented as a series of 2Dplanes , or surfaces, within the volume of interest. There are nolimitations to the orientation of the 2D planes or surfaces within thetarget volume, but they may be generally parallel planes.

Exploration and resource definition drilling is generally done in aseries of steps where the volume of interest is sampled at incrementallyincreasing densities to reduce the mineral estimation risk. Earlydrilling may only be interested in whether mineralization is present ornot. Subsequent drilling may be interested in determining the geometricboundaries of the mineralization. Next, mineral resource estimationdrilling increases the drilling density to estimate the mineral contentin the volume of interest. As such, it will be understood that overtime, the target volume may change. Change does not always occur,however. For example, for a late stage program the targeted volume maybe constant, and focus may be placed on decreasing risk by increasingthe density of sampling.

Determining the sampling requirements such as sampling density maygenerally be done by a person skilled in the art. Decisions aretypically based on:

-   -   professional knowledge;    -   experience from other similar properties; and    -   on data taken from initial drilling campaigns.

The sampling density may be given as, for example, a grid spacing(100×100 m), a distance to closest drillhole(s) (i.e. a hole within 80m, 2 holes within 70 m), or any other suitable specification thatdescribes the spatial characteristics desired within the volume ofinterest. Given sufficient sampling results, geostatisticians may usevariography to determine the spatial and directional correlation formineralization within the volume of interest. This may then result inthe density of drilling changing depending on the orientation of thepresumed mineralization within the target volume. In practice, differentsub-volumes within the volume of interest may need to be drilled atdifferent densities. A sampling requirements constraint as describedherein may account for these considerations.

A variogram may provide a skilled person with a means, based on actualsamples, to refine the sampling spacing in such a way so as tostatistically provide an assurance of mineral continuity along certaindirections in space. The continuity may not be equal in all directions,and thus the use of an ellipsoid is often used to describe the samplingspacing.

In the illustrated example, an algorithmic drill hole placement tool(also referred to herein as an algorithmic solver) generates anditeratively improves a drill plan which is constrained by the specifiedoperational constraints. The resulting drill plan is an optimal ornear-optimal drill plan which meets the requirements of the operationalconstraints.

It will be understood that in certain examples, such as in earlyexploration or simple resource deposits, the algorithmic solver mayattempt to homogenously sample the target volume and primarily considerfactors such as sub-volume distance from a drill hole. In morecomplicated examples, such as cases where there is anisotropicdistribution of mineralization, the algorithmic solver may change thedistribution of drill holes to reflect the measured mineralizationdistribution.

Another example of a non-limiting embodiment of a method as describedherein is shown in FIG. 2. In the illustrated method embodiment, saidmethod comprises a geological targeting model comprising:

-   -   one or more target volumes of interest defined in 3D space; the        one or more target volumes being segmented into sub-volumes to        which one or more attributes indicating relative desirability        are assigned; and    -   a set of operational constraints.

The illustrated method further comprises an algorithmic drill holeplacement component. This component comprises:

-   -   an algorithmic solver used to iteratively or programmatically        generate and improve a drill plan which aims to provide an        optimal or near optimal solution for drill hole distribution        within the one or more target volumes such that all or nearly        all of the sub-volumes, or at least the sub-volumes of greatest        desirability, of the one or more target volumes are sampled to a        specified or desired level;

wherein the resulting drill plan comprises a collection of one or moreplanned drill holes which are defined in 3D space; and

wherein the set of operational constraints of the geological targetingmodel constrains the iteratively or programmatically generated andimproved drill plan.

The illustrated method further comprises a resource conversioncalculator which scores the value of the resulting drill plan(s)generated by the algorithmic drill hole placement component. Localoptimization and execution may further occur in the illustrated methodas shown.

In the illustrated method, the resulting drill plan may be reconsideredin light of new field data and analysis data as shown with an aimtowards improving the drill plan. For example, newly acquiredinformation obtained as drilling operations progress and/or obtainedfrom on-site observations may be used to reconsider the resulting drillplan. The illustrated method shown in FIG. 2 further comprises aconstraint modifier. The constraint modifier changes or flexesparameters and/or constraints used by the geological targeting model(i.e. parameters of the target volume, operational constraints, etc . .. ), taking into consideration the newly acquired information. Asubsequent iteration may then occur, resulting in an improved updateddrill plan.

In the method illustrated in FIG. 2, the resulting drill plan provides:

the location where each drill hole starts as a 3D point (i.e. collarlocation);

the direction of drilling, defined as a vector in 3D which provides twoangles (i.e. azimuth and dip);

the expected length of each hole; and

optionally, the expected drill path may be predicted.

In the illustrated example, the drill plan further includes a rankingfor each of the drill holes so that priority can be given to theirdrilling.

The example illustrated in FIG. 2 includes operational constraints inthe geological targeting model which limit particular drilling operationbudgets. Typically, resource drilling programs do not drill the fullvolume to a given density due to budget limitations. In the method shownin FIG. 2, ranking of the relative values of the sub-volumes within thetarget volume of interest (i.e. assigning relative desirability to thesub-volumes) may be used to preferentially drill off the volume for alimited budget. As described, the operational constraints in FIG. 2include a variable budget constraint, and a means of placing a“desirability” value on each sub-volume within the volume of interest.By directing the algorithmic drill hole placement component to maximizethe value of the blocks sampled with a high level of desirability for agiven budget, the method can determine a level of priority drilling fora given target budget to reach a projected classification.

It will be understood that in certain examples, determining samplingdesirability may be dependent on the drilling phase, and on the detailsof the geological domain being drilled. By way of a simplified example,the distance from an existing drill hole can be used to rank each of thesub-volumes in the volume of interest. Using such a simplified ranking,the algorithmic drill hole placement component may then place new holesin sub-volumes that are far from existing sampled locations. Althoughdistance to a drill hole may represent a useful factor for ranking drillhole placement, it typically does not consider trade-offs associatedwith setup costs and other factors affecting downstream value. Thus,more complex desirability determinations as also described herein andbelow are often of interest.

More complex desirability determinations may include other criteria suchas:

estimate variance and/or grade estimates on sub-volumes (these mayprovide, for example, other criteria to guide drilling for cases wherebudgets are limited);

the determined location of geological features or structures such ascontacts and/or faults (these may add value to the interpretationwithout directly affecting the quality of the resource estimate);

probability of surfaces and geological features;

geometric or topological relationship between geological features;

proximity to geological features;

or any combination thereof.

Examples of attributes indicating relative desirability which may beassigned to sub-volumes are also described in previous sections above.

Typically, efforts are made to determine the bounding volumes of thevolume of interest. The bounding volume is often times determined at theboundary of two or more rock types. For example, one rock type may bebarren, whereas the other contains mineralization. This type of drillingis referred to as geological drilling, as it focuses on determining thegeological/geometric characteristics of the volume of interest. Samplesare analysed to determine lithology, with less emphasis placed on thegrade of the sample.

Another type of drilling is known as resource or classificationdrilling. In this case, it is the grade of mineralization that iscontained in the sample that is of value. This grade may then be used toestimate the value of mineralization in a larger volume of ground. Thedensity of sampling (and in many cases the estimated grade) maydetermine whether a volume of ground can be moved into one of threerecognized resource classification categories. In order of increasingvalue, based on quality of the estimate, the categories are: Inferred,Indicated, and Measured.

In the illustrated example shown in FIG. 2, as drilling densityincreases it becomes possible to move sub-volumes between each of thethree categories above. As international regulations (i.e. JORC and NI43-101) place more value on ground classified as Indicated and MeasuredVs. Inferred, there is motivation to move ground to Indicated quicklywhile, in early cases, not moving ground to measured classification.Because of complex geometry, and based on the phase of drilling, thesub-volume that is drilled to a density required by Measured may belimited when the drilling objective is Indicated drilling, thus avoidingoverdrilling in certain cases. Determining this type of trade off mayinvolve a resource classification calculator as shown in FIG. 2, whichmay be used to provide feedback to the algorithmic drill hole placementcomponent.

As will be understood, the method illustrated in FIG. 2 may be aniterative, dynamic method which may be repeated multiple times toprovide a range of drill plan options which may be sensitive to returnfor drilling investment.

The optional location optimization phase may adapt the global optimal ornear optimal plan to local drilling constraints.

Another example of a non-limiting embodiment of a method as describedherein is shown in FIG. 3. The illustrated embodiment is related to thatshown in FIG. 2, and provides a more specific and detailed constraintsexamples for further illustrative purposes. In the illustratedembodiment, said method comprises a geological targeting modelcomprising:

-   -   one or more target volumes of interest defined in 3D space; the        one or more target volumes being segmented into sub-volumes to        which one or more attributes indicating relative desirability        (i.e. ranked volume of interest) are assigned; and    -   a set of operational constraints (i.e. those shown in the large        box at the top left of FIG. 3).

The illustrated method further comprises an algorithmic drill holeplacement component. This component comprises:

-   -   an algorithmic solver used to iteratively or programmatically        generate and improve a drill plan which aims to provide an        optimal or near optimal solution for drill hole distribution        within the one or more target volumes such that all or nearly        all of the sub-volumes, or at least the sub-volumes of greatest        desirability, of the one or more target volumes are sampled to a        specified or desired level;

wherein the resulting drill plan comprises a collection of one or moreplanned drill holes which are defined in 3D space; and

wherein the set of operational constraints of the geological targetingmodel constrains the iteratively or programmatically generated andimproved drill plan.

The illustrated method further comprises a resource conversioncalculator which scores the value of the resulting drill plan(s)generated by the algorithmic drill hole placement component, providingan expected conversion/classification. Local optimization and executionmay further occur in the illustrated method as shown.

In the illustrated method, the resulting drill plan may be reconsideredin light of new field data and analysis data as shown, with an aimtowards improving the drill plan. For example, newly acquiredinformation obtained as drilling operations progress and/or obtainedfrom on-site observations may be used to update the resulting drillplan. The illustrated method shown in FIG. 3 further comprises aconstraint modifier which depicted in association with the set ofoperational constraints and the ranked volume of interest. Theconstraint modifier changes or flexes parameters and/or constraints usedby the geological targeting model (i.e. parameters of the target volume,operational constraints, etc . . . ), taking into consideration thenewly acquired information (if available). A subsequent iteration maythen occur, with improved parameters being used by the algorithmic drillhole placement component, resulting in an improved updated drill plan.

In the method illustrated in FIG. 3, the resulting drill plan provides:

the location where each drill hole starts as a 3D point (i.e. collarlocation);

the direction of drilling, defined as a vector in 3D which provides twoangles (i.e. azimuth and dip);

the expected length of each hole; and

optionally, the expected drill path may be predicted.

The example illustrated in FIG. 3 includes several operationalconstraints (as shown in the large box at the top left of the Figure) inthe geological targeting model. Operational constraints may includeconstraints which are time-dependent (such as, for example, when andwhere drilling setup locations are available). The operationalconstraints and target volume of interest parameters in the illustratedmethod may include: historical drill hole locations, potential drillsetup locations, drilling constraints, drilling program budget, volumeof interest, sampling requirements, ranked sub-volume, availability ofsetups, geological constraints, environmental constraints, rankedgeometric targeted shapes, hole deviation model, domains based on searchellipsoids, and modeling variance, and combinations thereof.

The operational constraints allow for consideration of a wide variety offactors. As an example, geological constraints may be considered. Forexample, a constraint limiting the angle of intersection with identifiedor presumed geological structures may, in certain examples, allow forimproving the overall efficiency of a drilling campaign. The angle ofintersection with geological structures may ensure that drill holesactually drill through geological structures. By way of example, if theangle of incidence is too low, then the drill hole may deflect along thestructure (undesired behavior), instead of drilling through thestructure (desired behaviour).

In further examples, newly acquired drilling data may be used as a meansto change or affect the volume of interest parameters, or otherparameters or operational constraints being used by the algorithmicdrill hole placement component. The target of interest may thus changeif a user wishes to use a portion of the drilling budget to step-outfrom the existing known mineral envelope to expand the target volume.

A more detailed example of data flow in the geological targeting modelof FIG. 3 is shown in FIG. 4. This figure provides an illustration ofdata flow modeling geological decision making to provide data to thealgorithmic drill hole placement component. The illustrated data modelmay be used to codify selected explicit and implicit factors in planningdrill holes. The illustrated example is provided for illustrativepurposes, and it will be understood that customization and tailoring maybe performed in accordance with the problem being modelled. The order ofdata transformations may also change to reflect priorities for eachdrilling project. Depending on the case being modelled, not all inputparameters may be present (for example, not all cases will have faultsas part of the process).

The arrows in FIG. 4 represent external data provided to the system.This data may or may not be static. Typically, geometry and parametersevolve as drilling campaigns progress. Input data may be generated by ahuman, or may be calculated by an external process (i.e. implicitmodeling of boundary surfaces based on drill hole intersects orgeological interpretations). Having such a decision model may allow foreach iteration to reflect the use of currently known data, and may allowfor adaptability to changes in interpreted surfaces. By creating newprojected surfaces (i.e. hypothesized surfaces), the model may furtherbe used to determine “what-if” scenarios.

Data inputs shown in FIG. 4 may include the following:

Each circle represents a data processing step for either validating ortransforming a dataset. By way of example, a drill hole lofttransformation may calculate the expected trajectory of a drill holebased on a user supplied hole deviation expectation. This deviationexpectation may be based on, for example, user experience when reviewingprevious drilling campaigns. The exact model used to determine theexpected trajectory for the drill holes may be customizable viascripting support.

The graphical representation in FIG. 4 does not explicitly show all ofthe data input parameters that may be defined for each of thetransformation steps. As an example, the “Deviate Drill Holes”transformation may use an input of expected drill holedeviation—typically provided as angles over a distance drilled(curvature).

FIG. 5 depicts an illustrative example of how transformation elementsmay be used to programmatically determine drill hole deviationparameters from existing already drilled Surveyed Drill Holes.Additional filters may be applied to sample a subset of the historicaldrill holes to match parameters associated to new drill holes. Thus,drillhole deviation may be estimated not by manual input, but ratherdynamically calculated based on historical drilling. As new holes aredrilled, the estimated deviation may be refined for future holes. Theaddition of a Filter Drill Holes transformation may allow for onlyhistorical holes that are “like and in proximity” to the current holesbeing drilled to be used to create the deviation estimate.

The links shown in FIG. 5 represent the dataflow between datatransformations (algorithms). Explicitly showing these links may allowusers, such as users in the field, to quickly validate the model beingused to determine the priorities associated to placing/distributing thedrill holes within the target volume of interest. Each of thetransformations may provide a level of selection or logic. Whencombined, the resulting dataflow may translate geological interpretationand decision making criteria into a data format that the drill holeplacement algorithm may use.

An example of a target volume which has been segmented into sub-volumesis shown in FIG. 6. In this figure, a target volume has been segmentedinto sub-volumes, and gains have been associated to each sub volume. Inthis figure, gains are calculated as a function of distance fromexisting drillholes and distance from existing sub-volumes that areclassified as measured.

In certain embodiments, sub-volumes may be generally modeled as a blockmodel, where the centroid of each block may be associated to a number ofnumerical parameters.

In the example illustrated in FIG. 3, the data is formatted to becompatible with the algorithmic drill hole placement component beingused to perform the spatial distribution. In a non-limiting example, thesub-volume block model and associated parameters may be formatted foruse by an SCP algorithm as described in, for example, Metaheuristiqueshybrids pour les prob/emes de recouvrement et recouvrement partield'ensembles appliqués au problem de positionnement des trous de foragedans les mines (Nehme Bilal, Thesis, Ecole Polytechnique, Montreal,Canada, 2014; ISBN 9781321840629, 1321840624); herein incorporated byreference in its entirety. The output may also be modified to allow useby a deterministic solution such as the CPLEX solver, or other suitableheuristic approaches.

The graphical representations in FIGS. 4 and 5 provide a static view ofthe drill hole planning process, although it will be understood that theprocess may be run a number of times to provide a range of solutions.This range of solutions may be used to develop a sensitivityanalysis—during each of the runs, one or more parameters may bemodified. FIG. 3 provides a view of the iterative nature of the modelingprocess which incorporates both localized optimization, and using realin-field data as input into the geological targeting model as drillingprogresses.

The output from the algorithmic drill hole placement component may be aproposed set of drillholes characterized by collar (start 3Dcoordinates) and either a trace for deviated holes or a dip/azimuth andlength for straight holes. Alternatively, a straight plan may beprovided as two 3D points, for example. The output may then be processedby a resource conversion classifier to determine an estimated quantityof ground that will match each of the classification criteria (i.e.inferred, indicated and measured), and a less stringent geologicalclassification.

In certain examples, a suitable resource classifier may be commerciallyavailable software known to the person of skill in the art having regardto the teachings herein.

If grade information is available, then estimated tonnage may becalculated.

Use of a resource classifier may allow for an assessment of value (forexample, either gauged by projected tons of mineralization in theresource, or by decreasing overall estimation variance). Given thatmineral estimation error can be determined uniquely using the locationof the samples, it is not necessary to include actual in-situ grade todetermine the relative effectiveness of sampling programs. Sampling isnot dependant on the grade value that is discovered in the ground. Theresults of the resource conversion estimate may optionally be comparedto desired conversion requirements. If the conversion goals are not met,the constraint modifier may search for a combination of parameters thatmeet the desired conversion goals. Optionally, a user may decide whichconstraints they wish the constraint modifier to flex.

If the conversion goals are met, the constraint modifier may change oneor more constraints and re-run the process to determine whether a“better” solution can be found. Parameters for the resource conversionmay be dependent on the level of existing geological knowledge. Theinitial conversion criteria (i.e. geological to inferred) may be basedon position of drill holes while later stage evaluation, where more datais available, may include estimated grade values to determine ore/wasteestimates.

The number of processing steps, and the complexity of the targetingmodel, may be dependent on the amount of information that is known aboutthe specific volume of ground being drilled. As more information isdiscovered, interpreted, and known, the model may evolve to ensure thatconstraints and goals are correctly being met. This may ensure thatdrill plans progress with the best alignment between what is known, andwith the current exploration goals.

EXAMPLE 2—MULTIPLE ITERATIONS EXAMPLE

As described herein, methods and systems may employ an iterative orrepeated approach in generating and improving drill plans. Such anapproach may involve changing constraints between iterations. Inembodiments, performing multiple iterations as part of the planningprocess may allow for both sensitivity analysis (i.e. determining whichparameters most affect conversion and discovery value), and create astatistical representation of the expected outcome for the drillingprogram. Input for the model may be asynchronously added to the model asit becomes available either from:

Interpretation;

modeling (explicit or implicit); or

from the field or laboratory.

When changing a budgeting constraint, for example, given that solutionsare near global optimal solutions, the speed with which the volume ofinterest may be converted to a higher resource classification categorymay be determined. The slope of the resulting curve may provide decisionmaking insights about when and how much budget should be allocated toprogress a project. The graph shown in FIG. 7 provides an example inwhich a means to compare multiple projects or drilling configurations isgenerated. As an example, adding a new type of drilling technology (suchas wedged holes) may significantly improve the speed with which a targetgets converted to higher classifications.

The graph in FIG. 7 can be used to make financial and/or investmentdecisions, for example. For a single property, it projects how quicklyconversion of resource will occur for different levels of investment.Because the graph is based on global optimums and backed by thegeological targeting model as described above, there may be a high levelof confidence in the illustrated results in this illustrative example.It will be understood that graphs may be similarly created for more thanone property, which may then be used to make financial and/or investmenttrade-off decisions between these different properties.

From left to right, the graph in FIG. 7 transitions between linear,curved and asymptotic segments. Each inflection point may demonstrate adrilling strategy change point where it may be decided whether to moveforward or not. The blue and red lines in the graph may be used todemonstrate how an algorithmic approach as described herein maysubstantially improve targeting of sub-volumes with higher desirability.

EXAMPLE 3—LOCALIZED OPTIMIZATION AND RESETTING OF COLLAR LOCATIONSUNDERGROUND

Drilling companies typically need to start the drill hole (i.e. collarthe hole) within a meter of a specified collar location in a drill plan.This can be a time consuming task. The methods and systems describedherein may, instead, allow focus to be placed on the purpose of thedrilling (i.e. what is the objective of the drill hole), and then, basedon the location of the drill, determine what value may be gained or lostby resetting the location of the collar. Updating the dip and azimuthfor a new collar location could be done at the face. Thus, for certaindrill rigs, time required to move the head of the rig, or move the rig,to reset the collar location may be saved.

In this type of adaptable system as described herein, the hole's dip andazimuth may be recalculated to adjust to the new starting point.Presumably, for a deviated hole, the expected trajectory may bere-calculated. The recalculated solution may be provided to a Drill Rig,or the Drill Rig may be capable of recalculating the solution, or both.

An illustrative demonstration is shown in FIG. 8, where the collarlocations have been moved back along a tunnel by 10 m. The dots show thepierce point locations on the tunnel's surface. 10 metre shifting iswell out of compliance with the previously described 1 m requirementtraditionally facing drilling companies. In addition to allowing fastersetup, there may also be a direct savings of 10 m of tunnel advance(typical costs may be around $7-10K/m in development cost). Theexemplified calculation assumes that the driller know some basicgeometric information, such as the heading of the tunnel and thecoordinates of one point in the tunnel. From there, the alternate collarcoordinates may be determined and a method as described herein may thenre-calculate the updated expected/planned drill traces. The histogramsshown in FIG. 9 demonstrate that there is little change in thespacing/density within the volume of interest given the “significantchange” in collar location, demonstrating that drill hole spacing usingadaptive targeting as described herein does not decrease despite a 10 mchange in collaring location in this example.

A summary of the starting and new layouts, including informationregarding the number of drill holes, the type of drill holes, and thedrill length was also prepared (see FIG. 13).

EXAMPLE 4—COMPARISON EXAMPLES

Since each of the drill holes may be ranked by the volume of ground thatit is qualifying or discovering, this may allow assessment of therelative value between drilling programs (i.e. drill plans) for theaggregate of all holes. The full value that a drilling program providesmay thus be determined. Perhaps more interesting, however, is todetermine the relative loss or gain to the program produced by eachdrill hole. In the following illustrative and non-limiting example, twohistograms showing comparison of both a manual plan and a plan generatedusing methods as described herein are described. These histograms areillustrated in FIGS. 10 and 11. The bins are value produced by hole. Theheight of each bar indicates how many holes, of a given value, areincluded in the drill plan. (i.e. there is one manual hole thatgenerates 5,000 units of goodness (as shown in, for example, FIG. 6scale, right of image)—left most hole on the histogram in FIG. 10).Analysing the graph allows determination that the drill holes of theplan generated using methods as described herein are, on average, moreproductive than the manual holes (i.e. there is more orange to the rightof the graph). In this example, the median hole of the plan producedusing methods as described here produces 45,000 units of goodness. Thereare only 7 manual holes that are at this median (as compared with 12holes of the plan generated using methods as described herein).

When drilling begins, how each of the drill holes ranks compared to theexpected distribution can be determined. The graph shown in FIG. 11shows 6 holes drilled in the drill plan. P-8 is a very low value drillhole which is a manually planned hole. The highest value in the program,based on the geological targeting model used, is to drill the drillholes in order from right to left. There are 7 holes that would havebeen more valuable than P9 to meet the overall objective as defined inthe drill program. Any of the holes shown would have been more valuablethan the manually planned P8. By not drilling this order and by drillingmanual holes, there is a net loss to the overall value of the drillingprogram as shown. With methods as described herein, relative value lostor gained may be determined. In the illustrated example, the companyshould, given physical constraints, begin drilling all holes to theright of the median holes located at 20,000 units of goodness in orderto maximize the value of the drill program. A review to determine whythe P-8 hole was drilled may also be done, as it may affect thegeological targeting model's premise in this illustrative example.

EXAMPLE 5—ADDITIONAL DECISION CRITERIA, CONSTRAINTS, METHODS, ANDSYSTEMS FOR DRILL PLAN GENERATION AND OPTIMIZATION

Another example of a method as described herein which providesnon-limiting examples of suitable decision criteria and/or constraintswhich may be used or considered in the generation and/or optimization ofa drill plan is described in further detail below with reference to FIG.12.

In the bubble flow diagram depicted in FIG. 12, circled balls indicatedwith arrows represent examples of geometric and/or volumetric inputsused in a geological targeting model for generating a drill plan usingan algorithmic solver. Each ball may represent one, or more than oneinput. By way of example, the “fault” ball may represent one or multipleinputs relating to one or more than one fault.

The algorithmic solver of the exemplified method is shown on the farright of FIG. 12. The algorithmic solver of the exemplified method maybe an algorithmic solver such as is described in Metaheuristiqueshybrids pour les problemes de recouvrement et recouvrement partield'ensembles appliqués au problem de positionnement des trous de foragedans les mines (Nehme Bilal, Thesis, Ecole Polytechnique, Montreal,Canada, 2014; ISBN 9781321840629, 1321840624) (herein incorporated byreference in its entirety), or an algorithmic solver such as isdescribed herein. In certain examples, a constraint modifier may flex ormodify constraint parameters/values, and the algorithmic solver maydetermine how the output (i.e. the drill plan) is affected by thisflexing or modifying in an iterative approach, allowing determination ofhow alternative drill plans affect the classification of the targetvolume. In this regard, a resource conversion calculator may be employedfor assessing classification of the target volume.

EXAMPLE 6—DRILL TIME REDUCTION

A non-limiting example of showing a number of generated plans evaluatingdrilling constraints, drilling technology (mobile drill rigs), and totaltime required to drill the program using an example of method and systemas described herein, as compared to an initial manual drill plan, isdescribed in further detail below with reference to FIG. 14 and FIG. 15.

In this illustrative and non-limiting example, total estimated drillingtimes and performance are compared between drill plans generatedmanually and by two alternative configurations(Solution 1 and Solution2) of methods as described herein. Solution 1 further demonstrates thevalue that can be gained by using a high mobility drill rig. Drill plansare generated using the same initial drilling locations for all 4examples. As can be seen in FIG. 14, all 3 drill plans generated usingmethods described herein result in either decreased drilling aggregatelength or increased expected coverage/conversion.

The “Solution 1 with High Mobility rigs” demonstrates the value of ahigh mobility drill rig providing the capability of quickly aligningholes. Although the aggregate number of metres and the expected coveragedo not change, the total time taken to drill the program decreases whencompared to any other solution and in particular to Solution 1 whichuses the same drill plan.

The timing values used in this non-limiting example for all drill planswere created using simplified assumptions. Calculated time ranges areproportional and generally comparable to the time taken to drill a 14 kmprogram. Dates are only used for reference purposes.

These results in this example demonstrate that methods as describedherein may be used to access the value of changing drillingtechnologies.

Furthermore this example demonstrates how a timing criteria can be addedto determine additional drill program benefits, and to ensure that adrill program can be completed within timing constraints, such as drillsite/setup availability.

FIG. 15 complements FIG. 14, provides further performance indicatorsdirectly related to the amount of aggregate metres drilled in the targetvolume (green; left end of bars) vs. aggregate metres (grey; right endof bars) in order to access the target volume. Expectedcoverage/conversion numbers, setup locations and total aggregate drilledmetres are also provided. In this Figure, Solution 1 and Solution 2 arethe same solution, however Solution 2 uses a high mobility drill whereasSolution 1 uses a lower mobility drill. Comparing coverage/conversionfigures between Solution 1 and Solution 2 demonstrates the value ofun-constraining the azimuth, generating a 10% increase incoverage/conversion.

FIG. 16 provides a data flow diagram corresponding to the method used inthis Example.

One or more illustrative embodiments have been described by way ofexample. It will be understood to persons skilled in the art that anumber of variations and modifications can be made without departingfrom the scope of the invention as defined in the claims.

What is claimed is:
 1. A computer-implemented method for improvingmineral resource exploration and resource classification efficiency,said computer-implemented method comprising: defining a one or moretarget volumes of interest in 3D space; segmenting the one or moretarget volumes into sub-volumes to which one or more attributesindicating relative desirability may be assigned; and iterativelygenerating and improving a drill plan using an algorithmic solverexecuted by a processor, wherein the drill plan aims to provide anoptimal or near optimal solution for drill hole distribution within theone or more target volumes such that all or nearly all the sub-volumes,or at least the sub-volumes of greatest desirability, of the one or moretarget volumes are sampled to a specified or desired level, includingobtaining information from a drill rig to be supplied to the algorithmicsolver for the iteratively generating; graphically presenting the drillplan using an interface communicatively connected to the processor,wherein the resulting drill plan comprises a collection of one or moreplanned drill holes which are defined in 3D space, wherein a set ofoperational constraints constrains the iteratively generated andimproved drill plan, and wherein the sub-volumes are blocks, and thealgorithmic solver aims to generate a drill plan which attempts tomaximize the number of blocks sampled by the drill holes of the drillplan while minimizing the total planned drill length of the drill plan,wherein the resulting drill plan is for identifying drill holes to bedrilled by drilling equipment.
 2. The method according to claim 1,wherein the set of operational constraints comprises a historical drillhole locations constraint, a potential drilling setup locationconstraint, a drilling direction constraint, a drilling dips constraint,a drilling azimuth constraint, a drilling budget constraint, a samplingrequirement constraint, a drilling setup availability constraint, aconstraint regarding distribution of drill holes from setups, aconstraint regarding the total amount of surface ground disturbance, atopographical constraint, an environmental constraint, a constraintregarding environmental exclusion zones, a geological fault constraint,a geological contacts constraint, a geological structure constraint, ora constraint regarding existing underground workings or operations, orany combination thereof.
 3. The method according to claim 1, wherein thealgorithmic solver is based on a heuristic algorithm, a linearalgorithm, a metaheuristic algorithm, a metaheuristic SCP algorithm, alocalized random search, a modified random search, an annealingalgorithm, a taboo search, or a multiple metaheuristic algorithmcomprising a genetic algorithm component, a taboo search algorithm andan iterated local search algorithm.
 4. The method according to claim 1,wherein the one or more attributes indicating relative desirability of asub-volume are selected from one or more of: distance of the sub-volumefrom an existing drill hole; estimate variance; grade estimates;bounding of the sub-volume by site specific geological contacts,structures, or faults; or variability or uncertainty of sub-volume gradeestimation or interpolation.
 5. The method according to claim 1, whereinthe drill plan aims to sample sub-volumes of the one or more targetvolumes such that a highest aggregate desirability is achieved, whereinan aggregate desirability primarily considers a value of resourceclassification, an identification of geological features bounding theone or more target volumes, or a combination thereof.
 6. The methodaccording to claim 1, wherein the drill plan is iteratively improved byimproving the global distribution of drill holes within the drill planbased on newly acquired information as drilling operations progress. 7.The method according to claim 1, wherein the specified or desired levelis selected from a range spanning geological, inferred, indicated,measured resource, and probable or proven reserve.
 8. The methodaccording to claim 7, wherein the specified or desired level is at leastabout 60% indicated while minimizing measured.
 9. The method accordingto claim 1, wherein the algorithmic solver aims to generate a drill planwhich attempts to maximize a number of sub-volumes sampled or classifiedper unit of planned drill length.
 10. The method according to claim 1,wherein the drill plan provides a ranking for each planned drill holewhich is based on a relative value of each planned drill hole to anoverall drill plan, and wherein one or more of a lowest ranked drillholes are eliminated from the drill plan.
 11. The method according toclaim 10, wherein the ranking includes a penalty for moving a drill holeof the drill plan away from a position at the one or more target volumeswhich is easily drilled, or away from a position at the one or moretarget volumes at which drilling equipment is already located.
 12. Themethod according to claim 10, wherein the relative value to the drillplan of changing one or more collar locations while dynamically updatingdip and dip direction is assessed.
 13. The method according to claim 1,wherein the iteratively generated drill plans are scored by a resourceconversion calculator, and the algorithmic solver improves a drill planscore using one or more parameters which are changed using a constraintmodifier between iterations, and wherein the constraint modifier changesor flexes one or more parameters selected from a historical drill holelocations constraint, a potential drilling setup location constraint, adrilling direction constraint, a drilling dips constraint, a drillingazimuth constraint, a drilling budget constraint, a sampling requirementconstraint, a drilling setup availability constraint, a constraintregarding distribution of drill holes from setups, a constraintregarding the total amount of surface ground disturbance, atopographical constraint, an environmental constraint, a constraintregarding environmental exclusion zones, a geological fault constraint,a geological contacts constraint, a geological structure constraint, ora constraint regarding existing underground workings or operations, orany combination thereof, the specified or desired level, or acombination thereof, between iterations.
 14. The method according toclaim 13, wherein the scoring of the drill plans includes rewardingdrill plans which provide information about location of geologicalstructures and contacts of the one or more target volumes, or rewardingdrill plans which have a reasonable probability of success.
 15. Themethod according to claim 1, wherein the method is an iterative methodwhich is repeated using input which is based on newly acquiredinformation obtained from drilling one or more planned drill holes froma previously generated drill plan, wherein the one or more planned drillholes from the previously generated drill plan are drill holes whichhave been drilled quickly but with reduced precision for geologicaldrilling or bounding of the one or more target volumes, allowing in-fillplanning, and improving the drill plan with less invested time.
 16. Themethod according to claim 1, wherein the orientation of a drill hole ofthe drill plan can be recalculated in real-time to accommodate foron-site drilling limitations, and wherein the on-site drillinglimitations are any one of drill site accessibility, drill holegeometry, drill hole timing limitations, a requirement for movement ofthe drill rig, setup availability, or any combination thereof.
 17. Themethod according to claim 16, wherein a completion constraint is used toidentify a point at which sufficient drilling has been completed, andwherein the point at which sufficient drilling has been completed is apoint at which further increase in drill hole density providesadditional value which is below a specified threshold.
 18. The methodaccording to claim 1, wherein the method further comprises: usingimplicit modeling to model geological contacts, faults, shells, andsurfaces; and updating the implicit modeling of the one or more targetvolume surfaces, geological structures, geological contacts, or acombination thereof, as drill hole data is acquired, thereby dynamicallyidentifying high value sub-volumes to be converted from unclassified togeological, inferred, indicated, or measured, and wherein the drill planis recalculated and the scoring of the resulting drill plan includes areward for solutions which allow for conversion of the identified highvalue sub-volumes from unclassified to inferred, indicated or measured.19. The method according to claim 1, wherein drill holes of the drillplan are ranked based on their value to the drill plan, and this rankingis used to indicate which holes of the drill plan should be drilledfirst.
 20. A computer system for improving mineral resource explorationand resource classification efficiency by generation and improvement ofa drill plan, said computer system comprising: a memory for storingprogram instructions; and a processor for executing the programinstructions; wherein the program instructions comprise instructionsfor: defining one or more target volumes of interest in 3D space;segmenting the one or more target volumes into sub-volumes to which oneor more attributes indicating relative desirability may be assigned; anditeratively generating and improving a drill plan using an algorithmicsolver, wherein the drill plan aims to provide an optimal or nearoptimal solution for drill hole distribution within the one or moretarget volumes such that all or nearly all the sub-volumes, or at leastthe sub-volumes of greatest desirability, of the one or more targetvolumes are sampled to a specified or desired level, including obtaininginformation from a drill rig to be supplied to the algorithmic solverfor the iteratively generating; wherein the resulting drill plancomprises a collection of one or more planned drill holes which aredefined in 3D space, and wherein a set of operational constraintsconstrains the iteratively generated and improved drill plan; andwherein the computer system comprises an interface for graphicallypresenting the drill plan and for receiving input of user-defined targetvolume of interest parameters, operational constraint parameters, or acombination thereof, wherein the resulting drill plan is for identifyingdrill holes to be drilled by drilling equipment.